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Found 5 papers, 2 papers with code
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.
MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades.
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
Sea Ice Forecasting using Attention-based Ensemble LSTM
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play.
